// Copyright (c) 2021, gottingen group.
// All rights reserved.
// Created by liyinbin lijippy@163.com

#include "abel/random/discrete_distribution.h"

#include <cmath>
#include <cstddef>
#include <cstdint>
#include <iterator>
#include <numeric>
#include <random>
#include <sstream>
#include <string>
#include <vector>

#include "gmock/gmock.h"
#include "gtest/gtest.h"
#include "abel/log/logging.h"
#include "testing/chi_square.h"
#include "testing/distribution_test_util.h"
#include "abel/random/engine/sequence_urbg.h"
#include "abel/random/random.h"
#include "abel/strings/str_cat.h"
#include "abel/strings/strip.h"

namespace {

    template<typename IntType>
    class DiscreteDistributionTypeTest : public ::testing::Test {
    };

    using IntTypes = ::testing::Types<int8_t, uint8_t, int16_t, uint16_t, int32_t,
            uint32_t, int64_t, uint64_t>;
    TYPED_TEST_SUITE(DiscreteDistributionTypeTest, IntTypes);

    TYPED_TEST(DiscreteDistributionTypeTest, ParamSerializeTest) {
        using param_type =
        typename abel::discrete_distribution<TypeParam>::param_type;

        abel::discrete_distribution<TypeParam> empty;
        EXPECT_THAT(empty.probabilities(), testing::ElementsAre(1.0));

        abel::discrete_distribution<TypeParam> before({1.0, 2.0, 1.0});

        // Validate that the probabilities sum to 1.0. We picked values which
        // can be represented exactly to avoid floating-point roundoff error.
        double s = 0;
        for (const auto &x : before.probabilities()) {
            s += x;
        }
        EXPECT_EQ(s, 1.0);
        EXPECT_THAT(before.probabilities(), testing::ElementsAre(0.25, 0.5, 0.25));

        // Validate the same data via an initializer list.
        {
            std::vector<double> data({1.0, 2.0, 1.0});

            abel::discrete_distribution<TypeParam> via_param{
                    param_type(std::begin(data), std::end(data))};

            EXPECT_EQ(via_param, before);
        }

        std::stringstream ss;
        ss << before;
        abel::discrete_distribution<TypeParam> after;

        EXPECT_NE(before, after);

        ss >> after;

        EXPECT_EQ(before, after);
    }

    TYPED_TEST(DiscreteDistributionTypeTest, Constructor) {
        auto fn = [](double x) { return x; };
        {
            abel::discrete_distribution<int> unary(0, 1.0, 9.0, fn);
            EXPECT_THAT(unary.probabilities(), testing::ElementsAre(1.0));
        }

        {
            abel::discrete_distribution<int> unary(2, 1.0, 9.0, fn);
            // => fn(1.0 + 0 * 4 + 2) => 3
            // => fn(1.0 + 1 * 4 + 2) => 7
            EXPECT_THAT(unary.probabilities(), testing::ElementsAre(0.3, 0.7));
        }
    }

    TEST(DiscreteDistributionTest, init_discrete_distribution) {
        using testing::Pair;

        {
            std::vector<double> p({1.0, 2.0, 3.0});
            std::vector<std::pair<double, size_t>> q =
                    abel::random_internal::init_discrete_distribution(&p);

            EXPECT_THAT(p, testing::ElementsAre(1 / 6.0, 2 / 6.0, 3 / 6.0));

            // Each bucket is p=1/3, so bucket 0 will send half it's traffic
            // to bucket 2, while the rest will retain all of their traffic.
            EXPECT_THAT(q, testing::ElementsAre(Pair(0.5, 2),  //
                                                Pair(1.0, 1),  //
                                                Pair(1.0, 2)));
        }

        {
            std::vector<double> p({1.0, 2.0, 3.0, 5.0, 2.0});

            std::vector<std::pair<double, size_t>> q =
                    abel::random_internal::init_discrete_distribution(&p);

            EXPECT_THAT(p, testing::ElementsAre(1 / 13.0, 2 / 13.0, 3 / 13.0, 5 / 13.0,
                                                2 / 13.0));

            // A more complex bucketing solution: Each bucket has p=0.2
            // So buckets 0, 1, 4 will send their alternate traffic elsewhere, which
            // happens to be bucket 3.
            // However, summing up that alternate traffic gives bucket 3 too much
            // traffic, so it will send some traffic to bucket 2.
            constexpr double b0 = 1.0 / 13.0 / 0.2;
            constexpr double b1 = 2.0 / 13.0 / 0.2;
            constexpr double b3 = (5.0 / 13.0 / 0.2) - ((1 - b0) + (1 - b1) + (1 - b1));

            EXPECT_THAT(q, testing::ElementsAre(Pair(b0, 3),   //
                                                Pair(b1, 3),   //
                                                Pair(1.0, 2),  //
                                                Pair(b3, 2),   //
                                                Pair(b1, 3)));
        }
    }

    TEST(DiscreteDistributionTest, ChiSquaredTest50) {
        using abel::random_internal::kChiSquared;

        constexpr size_t kTrials = 10000;
        constexpr int kBuckets = 50;  // inclusive, so actally +1

        // 1-in-100000 threshold, but remember, there are about 8 tests
        // in this file. And the test could fail for other reasons.
        // Empirically validated with --runs_per_test=10000.
        const int kThreshold =
                abel::random_internal::chi_square_value(kBuckets, 0.99999);

        std::vector<double> weights(kBuckets, 0);
        std::iota(std::begin(weights), std::end(weights), 1);
        abel::discrete_distribution<int> dist(std::begin(weights), std::end(weights));

        abel::insecure_bit_gen rng;

        std::vector<int32_t> counts(kBuckets, 0);
        for (size_t i = 0; i < kTrials; i++) {
            auto x = dist(rng);
            counts[x]++;
        }

        // Scale weights.
        double sum = 0;
        for (double x : weights) {
            sum += x;
        }
        for (double &x : weights) {
            x = kTrials * (x / sum);
        }

        double chi_square =
                abel::random_internal::chi_square(std::begin(counts), std::end(counts),
                                                 std::begin(weights), std::end(weights));

        if (chi_square > kThreshold) {
            double p_value =
                    abel::random_internal::chi_square_p_value(chi_square, kBuckets);

            // Chi-squared test failed. Output does not appear to be uniform.
            std::string msg;
            for (size_t i = 0; i < counts.size(); i++) {
                abel::string_append(&msg, i, ": ", counts[i], " vs ", weights[i], "\n");
            }
            abel::string_append(&msg, kChiSquared, " p-value ", p_value, "\n");
            abel::string_append(&msg, "High ", kChiSquared, " value: ", chi_square, " > ",
                                kThreshold);
            DLOG_INFO("{}", msg.c_str());
            FAIL() << msg;
        }
    }

    TEST(DiscreteDistributionTest, StabilityTest) {
        // abel::discrete_distribution stabilitiy relies on
        // abel::uniform_int_distribution and abel::bernoulli_distribution.
        abel::random_internal::sequence_urbg urbg(
                {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull,
                 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull,
                 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull,
                 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull});

        std::vector<int> output(6);

        {
            abel::discrete_distribution<int32_t> dist({1.0, 2.0, 3.0, 5.0, 2.0});
            EXPECT_EQ(0, dist.min());
            EXPECT_EQ(4, dist.max());
            for (auto &v : output) {
                v = dist(urbg);
            }
            EXPECT_EQ(12, urbg.invocations());
        }

        // With 12 calls to urbg, each call into discrete_distribution consumes
        // precisely 2 values: one for the uniform call, and a second for the
        // bernoulli.
        //
        // Given the alt mapping: 0=>3, 1=>3, 2=>2, 3=>2, 4=>3, we can
        //
        // uniform:      443210143131
        // bernoulli: b0 000011100101
        // bernoulli: b1 001111101101
        // bernoulli: b2 111111111111
        // bernoulli: b3 001111101111
        // bernoulli: b4 001111101101
        // ...
        EXPECT_THAT(output, testing::ElementsAre(3, 3, 1, 3, 3, 3));

        {
            urbg.reset();
            abel::discrete_distribution<int64_t> dist({1.0, 2.0, 3.0, 5.0, 2.0});
            EXPECT_EQ(0, dist.min());
            EXPECT_EQ(4, dist.max());
            for (auto &v : output) {
                v = dist(urbg);
            }
            EXPECT_EQ(12, urbg.invocations());
        }
        EXPECT_THAT(output, testing::ElementsAre(3, 3, 0, 3, 0, 4));
    }

}  // namespace
